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This article is part of the supplement: Twenty First Annual Computational Neuroscience Meeting: CNS*2012

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The variation of spike times

Conor Houghton12* and James B Gillespie1

Author Affiliations

1 School of Mathematics, Trinity College Dublin, Dublin 2, Ireland

2 Department of Computer Science, University of Bristol, BS8 1UB, UK

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BMC Neuroscience 2012, 13(Suppl 1):P132  doi:10.1186/1471-2202-13-S1-P132

The electronic version of this article is the complete one and can be found online at:

Published:16 July 2012

© 2012 Houghton and Gillespie; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Spike trains are often variable with the same stimulus producing different responses from presentation to presentation. These variations can be thought of as being composed of two different types of noise; variations in the spike times and variations in the spike count. The Victor-Purpura distance metric is used to separate these two noise types, allowing the distribution in spike time variations to be calculated. The distribution is calculated for a collection of example data sets. For these data, the distributions are not Gaussian but, in most cases, they can be accurately modeled by a hyper-Laplace distribution.